Probability theory is a powerful tool for inferring the value of missing variables given a set of other variables. As the number of variables in a system increases, the joint probability distribution over these variables becomes overwhelmingly large. In this lecture we examine the implications of factoring one large joint probability distribution into a set of smaller conditional distributions and study suitable algorithms for inference.

In order to be admitted to the final exam, students are required to register with the course in DigiCampus and STUDIS. No additional requirements are imposed.